Reinforcement learning (RL) algorithms have shown dramatic successes in areas such as game-playing [1]}, robotics [2]}, and advertising [3]}.
This increasing adoption of RL techniques in real-world settings has illustrated
the need for constraints on policies which are hard to express in the typical RL formulation, such as fairness [4]},
risk-sensitivity [5]}, and safety under exploration [6]}.
We consider the problem of learning a policy to maximize
reward, while minimizing the extent to which the policy's actions expose information about a sensitive subset
of the state variables.
